can this works?

app.py

@@ -15,7 +15,8 @@ from utils.gradio import reset_textbox, cancel_outputing, transfer_input, \
 
 
 # set variables
-model = "lemur-7B"
+BASE_MODEL = "llama-7B"
+LORA_MODEL = "output/llama_lora_7B/checkpoint-3700"
 
 
 print("Loading model...")
@@ -24,7 +25,11 @@ import time
 
 start = time.time()
 
-tokenizer, model, device = load_tokenizer_and_model(model, load_8bit=True)
+tokenizer, model, device = load_tokenizer_and_model(
+    base_model=BASE_MODEL,
+    adapter_model=LORA_MODEL,
+    load_8bit=True,
+)
 
 print("Model loaded in {} seconds.".format(time.time() - start))
 

lemur-7B/adapter_config.json

@@ -0,0 +1,23 @@
+{
+  "base_model_name_or_path": "llama-7B",
+  "bias": "none",
+  "enable_lora": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "merge_weights": false,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "down_proj",
+    "gate_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

lemur-7B/{pytorch_model.bin → adapter_model.bin}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8800f80fe257ad94942049beaa2dc86703571a8696bcaf0f03f57c021a2ec6ec
-size 524332500
+oid sha256:2e48602fc4d5043f5fa2fdcbb386377f99d30364211f85fa9c3273fb0cadffd6
+size 71703053

{lemur-7B → llama-7B}/config.json

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "llama-7B",
   "architectures": [
     "LlamaForCausalLM"
   ],
@@ -16,7 +15,7 @@
   "pad_token_id": 0,
   "rms_norm_eps": 1e-06,
   "tie_word_embeddings": false,
-  "torch_dtype": "float32",
+  "torch_dtype": "float16",
   "transformers_version": "4.30.1",
   "use_cache": true,
   "vocab_size": 32000

llama-7B/pytorch_model-00001-of-00002.bin

@@ -0,0 +1,3 @@
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+oid sha256:0087155d6df07106c1d910bfeb6aab1be8e612dfbf2b56ddfb4ccbde7dbd50d0
+size 9976634558

llama-7B/pytorch_model-00002-of-00002.bin

@@ -0,0 +1,3 @@
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+oid sha256:461bc5e50200db7813ff99cc0b9316c48ccbd6aaaa31bf8cf7bee0b64bc3eda3
+size 3500315539

llama-7B/pytorch_model.bin.index.json

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+    "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

{lemur-7B → llama-7B}/special_tokens_map.json

@@ -13,7 +13,6 @@
     "rstrip": false,
     "single_word": false
   },
-  "pad_token": "</s>",
   "unk_token": {
     "content": "<unk>",
     "lstrip": false,

{lemur-7B → llama-7B}/tokenizer_config.json

@@ -1,4 +1,6 @@
 {
+  "add_bos_token": true,
+  "add_eos_token": false,
   "bos_token": {
     "__type": "AddedToken",
     "content": "<s>",

requirements.txt

@@ -0,0 +1,3 @@
+peft==0.3.0
+torch==2.0.1
+transformers==4.27.4

utils/inference.py

@@ -1,20 +1,59 @@
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import LlamaTokenizer, LlamaForCausalLM
 from peft import PeftModel
 from typing import Iterator
 from variables import SYSTEM, HUMAN, AI
 
 
-def load_tokenizer_and_model(base_model, load_8bit=True):
+def load_tokenizer_and_model(base_model, adapter_model, load_8bit=True):
+    """
+    Loads the tokenizer and chatbot model.
 
+    Args:
+        base_model (str): The base model to use (path to the model).
+        adapter_model (str): The LoRA model to use (path to LoRA model).
+        load_8bit (bool): Whether to load the model in 8-bit mode.
+    """
     if torch.cuda.is_available():
         device = "cuda"
     else:
         device = "cpu"
 
-    tokenizer = AutoTokenizer.from_pretrained(base_model)
-    model = AutoModelForCausalLM.from_pretrained(base_model, load_8bit=load_8bit)
+    try:
+        if torch.backends.mps.is_available():
+            device = "mps"
+    except:  
+        pass
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    if device == "cuda":
+        model = LlamaForCausalLM.from_pretrained(
+            base_model,
+            load_in_8bit=load_8bit,
+            torch_dtype=torch.float16
+        )
+    elif device == "mps":
+        model = LlamaForCausalLM.from_pretrained(
+            base_model,
+            device_map={"": device}
+        )
+        if adapter_model is not None:
+            model = PeftModel.from_pretrained(
+                model,
+                adapter_model,
+                device_map={"": device},
+                torch_dtype=torch.float16,
+            )
+    else:
+        model = LlamaForCausalLM.from_pretrained(
+            base_model, device_map={"": device}, low_cpu_mem_usage=True
+        )
+        if adapter_model is not None:
+            model = PeftModel.from_pretrained(
+                model,
+                adapter_model
+            )
 
+    model.eval()
     return tokenizer, model, device
 
 class State:
@@ -31,7 +70,7 @@ shared_state = State()
 def decode(
     input_ids: torch.Tensor,
     model: PeftModel,
-    tokenizer: AutoTokenizer,
+    tokenizer: LlamaTokenizer,
     stop_words: list,
     max_length: int,
     temperature: float = 1.0,